We use GloVe [1]} word embeddings of dimension \(d_w\)  which is set to 300 in our experiments, and update the embeddings during training. We set the dimension \(d_z\)  to be 20 for the entity token indicator embedding vectors. The hidden vector dimension of the forward and backward LSTM is set at 320. The dimension of BiLSTM output is 640. We use 500 different convolution filters with kernel width of 3, 4, and 5 for feature extraction. We use one convolutional layer in both entity mention-level GCN and entity-level GCN in our final model. Dropout layers [2]} are used in our network with a dropout rate of \(0.5\)  to avoid overfitting. We train our models with a mini-batch size of 32 and optimize the network parameters using the Adagrad optimizer [3]}.
